Discrete komodo algorithm for traveling salesman problem

Abstract

This work presents a Discrete Komodo Algorithm (DKA) to solve the traveling salesman problem (TSP). Inspired by the Komodo Mlipir Algorithm, a swarm intelligence algorithm based on the behavior of komodo dragons, DKA introduces an enhanced edge-construction operator and a novel edge-destruction operator to explore the discrete search space of TSP. To prioritize the exploration of potential edges, DKA uses a Priority Queue that stores all possible edges with their weights, and its counts appear on tour as its priority. The edges with smaller weights and counts have higher probabilities of being selected for the tour. DKA is tested with 45 different TSP instances from TSPLIB. Experimental results show that DKA guarantees produced the best-known solution for small test problems for up to 24 nodes, and the best solutions are within 6.92% of the known optimal solutions. Compared with some of the previous algorithms, DKA outperforms the recent state-of-the-art algorithms and is significantly better than classical metaheuristic algorithms by achieving a lower relative error.